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- W4286569406 abstract "The potentials of automotive radar for autonomous driving have not been fully exploited due to the difficulty of extracting targets' information from the radar signals and the lack of radar datasets. In this paper, a novel signal processing pipeline is proposed to address the max ambiguous velocity reduction issue introduced by staggered time division multiplexing (TDM) scheme of high resolution imaging radar system with a large number of transmit antennas. A dataset of 1,410 synchronized frames (stereo cameras, LiDAR, radar) with three classes, i.e., bus, car, and people, is constructed from field experiments. Next, we implement a vanilla SpectraNet and show its promising performance on moving object detection and classification with a mean average precision (mAP) of 81.9% at an intersection over union (IoU) of 0.5." @default.
- W4286569406 created "2022-07-22" @default.
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- W4286569406 date "2022-06-20" @default.
- W4286569406 modified "2023-09-27" @default.
- W4286569406 title "Spectranet: A High Resolution Imaging Radar Deep Neural Network for Autonomous Vehicles" @default.
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- W4286569406 doi "https://doi.org/10.1109/sam53842.2022.9827798" @default.
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